CN104361102B - A kind of expert recommendation method and system based on group matches - Google Patents
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Abstract
The invention discloses a kind of expert recommendation method and system based on group matches, belong to Internet technical field, the described method includes：S1：The webpage information of each expert in specialist list is obtained by web crawlers；S2：The webpage information is extracted, to obtain expert's academic information of each expert；S3：Matching degree between each expert and project to be matched is calculated according to expert's academic information；S4：The expert that the project to be matched recommended is determined as by dynamic programming algorithm according to the matching degree and group matches model.The present invention realizes that expert recommends by way of group matches, improve expert and recommend efficiency, time overhead is greatly reduced, in addition, when calculating the matching degree between each expert and project to be matched, the social relationships matching degree between each expert and project to be matched is also contemplated, so that when realizing that expert recommends, also effectively avoids or prevent academic corruption problem.
Description
Technical Field
The invention relates to the technical field of internet, in particular to an expert recommendation method and system based on group matching.
Background
The evaluation efficiency and the evaluation quality of the scientific research projects have important influence on the scientific research development level of one unit or even one country. As a rapid and advanced evaluation mode, the network evaluation runs through the whole life cycle of each stage of scientific research or engineering projects from project establishment, application, organization, demonstration, evaluation, acceptance, rewarding to filing and the like, and the aim is to replace the traditional manual operation by using a computer and a network system, thereby reducing the evaluation cost, improving the working efficiency and the evaluation quality and standardizing the evaluation process by using an electronic information system.
In recent years, the rapid development of novel information technologies such as cloud computing, big data, recommendation systems, deep learning, social networks and the like enables intelligent network review, wherein the intelligent expert recommendation system is the core and difficulty of the whole network review process. The meaning of intelligentization here is: the system can not only process and refine internal information (based on codes, accuracy and structuralization), but also continuously converge external information (based on semantics, fuzzification and unstructured), classifies and evaluates experts through data accumulation, and generates an intelligent expert database with more guiding significance, so that a more reasonable recommendation model and algorithm are constructed, but the existing expert recommendation system has the problem of too low expert recommendation efficiency, and the time cost is too high.
Disclosure of Invention
In view of the above problems, the present invention provides an expert recommendation method based on group matching, the method comprising:
s1: acquiring webpage information of each expert in the expert list through a web crawler;
s2: extracting the webpage information to obtain expert academic information of each expert;
s3: calculating the matching degree between each expert and the item to be matched according to the expert academic information;
s4: and determining the experts recommended for the items to be matched through a dynamic programming algorithm according to the matching degree and the group matching model, wherein the group matching model is the corresponding relation between the items to be matched and the recommended experts when the sum of the matching degrees of all the recommended experts of the items to be matched reaches the maximum.
In the step S1, the webpage information of each expert in the expert list is obtained through a web crawler according to the expert names in the expert list.
Wherein, step S2 specifically includes:
s201: searching the web page information matched with the expert name and the working unit of the current expert from the web page information, if the web page information is not searched, executing the step S202, otherwise, extracting expert academic information from the searched first web page information, and executing the step S203, wherein the expert list comprises: the expert names and work units of each expert;
s202: searching webpage information matched with the expert name of the current expert from the webpage information, and extracting expert academic information from the searched first webpage information;
s203: the experts from which the expert academic information is not extracted in the expert list are taken as the current experts, and the process returns to step S201.
Wherein the expert academic information comprises: expert names, work units, research domain keywords, paper names, and paper authors.
Wherein, in step S3, the matching degree between each expert and the item to be matched is calculated according to the expert academic information by the following formula,
M i,j ＝α*MK i,j +β*MJ i,j +γ*ML i,j -δ*MS i,j
wherein M is i,j For the matching degree between the expert i and the item j to be matched, alpha, beta, gamma and delta are constants, MK i,j Matching degree of keywords, MJ, in the scientific research field between expert i and item j to be matched i,j For the matching degree of the labels of the periodical meeting between the expert i and the item j to be matched, ML i,j For the academic level matching degree between the expert i and the item j to be matched, MS i,j And (4) the social relationship matching degree between the expert i and the item j to be matched.
Wherein the MS i,j The calculation is carried out by the following formula,
wherein,is a weighted value; delta i,j Is the correlation degree of the working units between the expert i and the expert v, when the working units are the same, delta i,j Is 1, otherwise is 0; sp is a thesis that the expert i cooperates with the nth expert; n is the number of paper authors; t is t i The weight occupied by expert i; t is t v The weight occupied by the vth expert; k is the serial number of the project applicant corresponding to the project j to be matched; a is the number of the item applicants corresponding to the item j to be matchedAmount of the compound (A).
Wherein the group matching model is:
wherein,c i,j matching a matrix for the group, wherein when an expert i recommends a jth project, the jth column of a corresponding ith row in the matrix takes a value of 1, and otherwise, the jth column takes a value of 0; m is the total number of items to be matched; n is the total number of experts; epsilon is the maximum value of the number of experts corresponding to each item to be matched; sigma is the maximum value of the number of items corresponding to each expert.
In step S4, determining, according to the matching degree and the group matching model, an expert recommended for the item to be matched through a dynamic programming algorithm, specifically including:
s401: determining the experts corresponding to each item to be matched according to the research field keywords, and sequencing the experts corresponding to each item to be matched according to the matching degree;
s402: and sequentially distributing the experts with the highest matching degree to the corresponding items to be matched until the number of the experts corresponding to the items to be matched reaches a maximum value epsilon or the number of the items corresponding to the experts reaches a maximum value sigma.
The invention also discloses an expert recommendation system based on group matching, which comprises:
the webpage acquisition module is used for acquiring webpage information of each expert in the expert list through a web crawler;
the information extraction module is used for extracting the webpage information to obtain expert academic information of each expert;
the matching degree calculation module is used for calculating the matching degree between each expert and the project to be matched according to the expert academic information;
and the expert recommending module is used for determining the experts recommended for the items to be matched through a dynamic programming algorithm according to the matching degree and a group matching model, wherein the group matching model is the corresponding relation between the items to be matched and the recommended experts when the sum of the matching degrees of all the recommended experts of the items to be matched reaches the maximum.
The method realizes expert recommendation in a group matching mode, improves expert recommendation efficiency, greatly reduces time overhead, and also considers social relationship matching degree between each expert and the item to be matched when calculating the matching degree between each expert and the item to be matched, thereby effectively avoiding or preventing academic corruption problem when realizing expert recommendation.
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FIG. 1 is a flow chart of a method for expert recommendation based on group matching in accordance with one embodiment of the present invention;
fig. 2 is a block diagram of a group matching-based expert recommendation system according to an embodiment of the present invention.
Detailed Description
The following detailed description of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
FIG. 1 is a flow chart of a method for expert recommendation based on group matching in accordance with one embodiment of the present invention; referring to fig. 1, the method includes:
s1: acquiring webpage information of each expert in the expert list through a web crawler;
it should be noted that, in a general search engine, a web crawler is required to download a large amount of data, and the web crawler of step S1 only needs to visit a single site (i.e., an academic website), and the rule is obvious, so that technologies such as distributed and web page ranking are not required.
However, the target website needs to authenticate the identity, so a browser needs to be used for analyzing the authentication process and then simulating the browser to log in a server for crawling. Generally, the authentication mechanism of the website can be obtained by simulating a cookie, and through analysis of the target website, the target website is also authenticated by the cookie. Therefore, firstly, a website server is manually logged in to obtain the cookie with the authority, and then the cookie is copied into the web crawler, and the web crawler uses the authority to crawl the webpage.
Through analysis, when the webpage information of the expert is obtained, the corresponding webpage information can be obtained only by changing the expert number range parameter in the submitted http. The acquired webpage information is stored in a hard disk in the form of html webpage, then the webpage information is analyzed by using a regular expression, the information of experts is acquired, and a basic expert database is constructed.
It can be understood that, in this step, the web page information of each expert in the expert list is obtained through the web crawler according to the expert names in the expert list.
S2: extracting the webpage information to obtain expert academic information of each expert;
it should be noted that, after the basic expert database is provided, the expert names and the working units can be used to screen the experts and extract the expert academic information. In the process of extracting expert academic information, a phenomenon that many experts in an expert list have duplicate names is found, so that the experts can be distinguished through working units.
This step can be realized by the following steps S201 to 203:
s201: searching the web page information matched with the expert name and the work unit of the current expert from the web page information, if the web page information is not searched, executing a step S202, otherwise, extracting expert academic information from the searched first web page information, and executing a step S203, wherein the expert list comprises: the expert names and work units of each expert;
s202: searching webpage information matched with the expert name of the current expert from the webpage information, and extracting expert academic information from the searched first webpage information;
s203: the experts from which the expert academic information is not extracted in the expert list are taken as the current experts, and the process returns to step S201.
Optionally, the expert academic information comprises: expert names, work units, research domain keywords, paper names, and paper authors.
S3: calculating the matching degree between each expert and the item to be matched according to the expert academic information;
s4: and determining the experts recommended for the items to be matched through a dynamic programming algorithm according to the matching degree and the group matching model, wherein the group matching model is the corresponding relation between the items to be matched and the recommended experts when the sum of the matching degrees of all the recommended experts of the items to be matched reaches the maximum.
When the expert recommendation is realized, the experts in the related fields are needed to review the projects, so that the matching degree between the experts and the projects is positively correlated with the field correlation degree between the experts and the projects; in order to avoid and prevent academic corruption, if the applicant and the review experts of the project have a degree of correlation of social relationship, such as a cooperative paper, in the same work unit, etc., then the degree of matching between the experts and the project is inversely proportional to the degree of correlation of social relationship between the experts and the project, so in step S3, the degree of matching between each expert and the project to be matched is calculated according to the academic information of the experts by the following formula,
M i,j ＝α*MK i,j +β*MJ i,j +γ*ML i,j -δ*MS i,j
wherein M is i,j Alpha, beta, gamma and delta are constants for the matching degree between the expert i and the item j to be matched, MK i,j For the matching degree of keywords, MJ, of the scientific research field between the expert i and the item j to be matched i,j For the matching degree of the labels of the periodical meeting between the expert i and the item j to be matched, ML i,j For the academic level matching degree between the expert i and the item j to be matched, MS i,j And (4) the social relationship matching degree between the expert i and the item j to be matched.
Optionally, the MS calculates the social relationship matching degree i,j The calculation is carried out by the following formula,
wherein,is a weighted value; delta i,j Is the correlation degree of the working units between the expert i and the expert v, when the working units are the same, delta i,j The value of (1) is 1, otherwise the value of (0) is obtained; sp is a thesis that the expert i cooperates with the nth expert; n is the number of paper authors; t is t i The weight taken by expert i (which can be determined in the order of the authors of the paper); t is t v The weight taken by the vth expert (the weight can be determined according to the order of the authors of the paper); k is the serial number of the project applicant corresponding to the project j to be matched; a is the number of the item applicants corresponding to the item j to be matched.
Alternatively, assume that expert i's scientific research domain keywords are expressed as vectors<K i,1 ,K i,2 ,K i,3 ,...,K i,N >, its weight (actually the keyword frequency) is expressed as a vector<w i,1 ,w i,2 ,w i,3 ,...,w i,N > scientific research field keyword expression of item j as vector<K j,1 ,K j,2 ,K j,3 ,...,K j,N >, its weight (actually the keyword frequency) is expressed as a vector<w j,1 ,w j,2 ,w j,3 ,...,w j,N >. Defining scientific research field keyword matching degree MK by using content-based vector recommendation algorithm i,j Comprises the following steps:
where R (K) i,x ,K j,y ) Representing two scientific research fields key words K i,x And K j,y Similarity (R = remembrance).
Due to the fact thatThe keywords in the scientific research field are not particularly accurate in extraction, so that R (K) is calculated i,x ,K j,y ) Firstly, calculating the editing distance of two scientific research keywords: levenshein distance, edit distance, refers to the minimum number of edit operations required to transition from one string to another. Permitted editing operations include replacing one character with another, inserting one character, and deleting one character. Assuming that the editing distance is d and the longest word length of the two scientific research keywords is max, the similarity is 1-d/max.
Suppose that the journal conference label of expert i is expressed as a vector<J i,1 ,J i,2 ,J i,3 ,...,J i,N >, the weight (actually the tag frequency) of which is expressed as a vector<w i,1 ,w i,2 ,w i,3 ,...,w i,N > journal conference label expression of item j as a vector<J j,1 ,J j,2 ,J j,3 ,...,J j,N >, its weight (actually the tag frequency) is expressed as a vector<w j,1 ,w j,2 ,w j,3 ,...,w j,N &And (d) drying the steel. Defining the label matching degree MJ of the periodical conference by using a label-based vector recommendation algorithm i,j Comprises the following steps:
here I (J) i,x ,J j,y ) Label J for representing two periodicals meetings i,x And J j,y Is the same (I = Identity).
It should be noted that the journal conference label is generally quite accurate, unlike R (K) i,x ,K j,y ) Between 0 and 1.0, I (J) i,x ,J j,y ) And taking 1 when the periodical conference labels are equal, and taking 0 when the periodical conference labels are not equal.
Assume that the academic level vector of expert i is < unit level, title, scientific research project scale >, where the unit level is the level of colleges: such as 985, 211, common Benedict, specialty, and the like; title of job: such as Master, doctor, changjiang scholars, academists, etc.; and (4) scientific research project scale: the filed application includes completed and ongoing national research projects: for example, 863 projects and the like, the evaluation index is the amount of scientific research funds;
for the set represented by such a group of vectors, the student levels of the experts are clustered by using a k-means clustering method, k representative experts are selected first, experts similar to the representative experts are put into one class, and thus the experts are put into k classes. When calculating the academic level similarity of two experts, if the two experts belong to the same class, the similarity is 1, and if the two experts are not in the same class, the similarity is 0.
Then the academic hierarchy of the applicant for each project we can use such a set of vectors V 1 、V 2 、V 3 、……、V p (assuming that item j has p applicants in total). When calculating the academic level matching degree between a certain expert and a project, the similarity between the expert and each project applicant can be calculated respectively, then the similarity is summed up and divided by the number of the applicant to obtain the similarity between the project and the expert.
Optionally, the group matching model is:
wherein,c i,j matching a matrix for the group, wherein when an expert i recommends a jth project, the jth column of a corresponding ith row in the matrix takes a value of 1, and otherwise, the jth column takes a value of 0; m is the total number of items to be matched; n is the total number of experts; epsilon is the maximum value of the number of experts corresponding to each item to be matched; sigma is the maximum value of the number of items corresponding to each expert.
In order to facilitate determining the expert recommended for the item to be matched, optionally, in step S4, the determining the expert recommended for the item to be matched through a dynamic programming algorithm according to the matching degree and the group matching model specifically includes:
s401: determining the experts corresponding to each item to be matched according to the research field keywords, and sequencing the experts corresponding to each item to be matched according to the matching degree;
s402: and sequentially distributing the experts with the highest matching degree to the corresponding items to be matched until the number of the experts corresponding to the items to be matched reaches a maximum value epsilon or the number of the items corresponding to the experts reaches a maximum value sigma.
The invention also discloses an expert recommendation system based on group matching, and referring to fig. 2, the system comprises:
the webpage acquisition module is used for acquiring webpage information of each expert in the expert list through a web crawler;
the information extraction module is used for extracting the webpage information to obtain expert academic information of each expert;
the matching degree calculation module is used for calculating the matching degree between each expert and the item to be matched according to the expert academic information;
and the expert recommending module is used for determining the experts recommended for the items to be matched through a dynamic programming algorithm according to the matching degree and a group matching model, wherein the group matching model is the corresponding relation between the items to be matched and the recommended experts when the sum of the matching degrees of all the recommended experts of the items to be matched reaches the maximum.
The system also comprises modules, sub-modules, units and sub-units for realizing the steps of the method, and repeated description is omitted for avoiding repeated description.
The above embodiments are only for illustrating the invention and are not to be construed as limiting the invention, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention, therefore, all equivalent technical solutions also belong to the scope of the invention, and the scope of the invention is defined by the claims.
Claims (5)
1. An expert recommendation method based on group matching, the method comprising:
s1: acquiring webpage information of each expert in the expert list through a web crawler;
s2: extracting the webpage information to obtain expert academic information of each expert;
s3: calculating the matching degree between each expert and the item to be matched according to the expert academic information;
s4: determining experts recommended for the items to be matched through a dynamic programming algorithm according to the matching degree and a group matching model, wherein the group matching model is a corresponding relation between the items to be matched and the recommended experts when the sum of the matching degrees of all the recommended experts of the items to be matched reaches the maximum;
the expert academic information comprises: expert names, work units, research field keywords, paper names and paper authors;
in step S4, determining, according to the matching degree and the group matching model, an expert recommended for the item to be matched through a dynamic programming algorithm, specifically including:
s401: determining the experts corresponding to each item to be matched according to the research field keywords, and sequencing the experts corresponding to each item to be matched according to the matching degree;
s402: sequentially distributing the experts with the highest matching degree to the corresponding items to be matched until the number of the experts corresponding to the items to be matched reaches a maximum value epsilon or the number of the items corresponding to the experts reaches a maximum value sigma;
in step S3, the matching degree between each expert and the item to be matched is calculated according to the expert academic information through the following formula,
M i,j ＝α*MK i,j +β*MJ i,j +γ*ML i,j -δ*MS i,j
wherein M is i,j For the matching degree between the expert i and the item j to be matched, alpha, beta, gamma and delta are constants, MK i,j For expertsDegree of matching, MJ, of scientific research field keywords between i and item j to be matched i,j For the matching degree of the labels of the periodical meeting between the expert i and the item j to be matched, ML i,j For the academic level matching degree between the expert i and the item j to be matched, MS i,j Matching degree of social relationship between the expert i and the item j to be matched;
the MS i,j The calculation is carried out by the following formula,
wherein, is a weighted value; delta. For the preparation of a coating i,j Is the correlation degree of the working units between the expert i and the expert v, when the working units are the same, delta i,j The value of (1) is 1, otherwise the value of (0) is obtained; sp is a thesis that the expert i cooperates with the nth expert; n is the number of paper authors; t is t i The weight occupied by expert i; t is t v The weight occupied by the vth expert; k is the serial number of the project applicant corresponding to the project j to be matched; a is the number of the item applicants corresponding to the item j to be matched.
2. The method as claimed in claim 1, wherein in step S1, web page information of each expert in the expert list is obtained through a web crawler according to the names of the experts in the expert list.
3. The method according to claim 2, wherein step S2 specifically comprises:
s201: searching the web page information matched with the expert name and the working unit of the current expert from the web page information, if the web page information is not searched, executing the step S202, otherwise, extracting expert academic information from the searched first web page information, and executing the step S203, wherein the expert list comprises: the expert names and work units of each expert;
s202: searching webpage information matched with the expert name of the current expert from the webpage information, and extracting expert academic information from the searched first webpage information;
s203: the experts from which the expert academic information is not extracted in the expert list are taken as the current experts, and the process returns to step S201.
4. The method of claim 1, wherein the group matching model is:
wherein,c i,j matching a matrix for the group, wherein when an expert i recommends a jth project, the jth column of a corresponding ith row in the matrix takes a value of 1, and otherwise, the jth column takes a value of 0; m is the total number of items to be matched; n is the total number of experts; epsilon is the maximum value of the number of experts corresponding to each item to be matched; sigma is the maximum value of the number of items corresponding to each expert.
5. An expert recommendation system based on group matching, the system comprising:
the webpage acquisition module is used for acquiring webpage information of each expert in the expert list through a web crawler;
the information extraction module is used for extracting the webpage information to obtain expert academic information of each expert;
the matching degree calculation module is used for calculating the matching degree between each expert and the item to be matched according to the expert academic information;
the expert recommending module is used for determining the experts recommended for the items to be matched through a dynamic programming algorithm according to the matching degree and a group matching model, and the group matching model is the corresponding relation between the items to be matched and the recommended experts when the sum of the matching degrees of all the recommended experts of the items to be matched reaches the maximum;
the expert academic information includes: expert names, work units, research field keywords, thesis names and thesis authors;
the expert recommendation module is specifically configured to:
determining the experts corresponding to each item to be matched according to the research field keywords, and sequencing the experts corresponding to each item to be matched according to the matching degree;
sequentially distributing the experts with the highest matching degree to corresponding items to be matched until the number of the experts corresponding to the items to be matched reaches a maximum value epsilon or the number of the items corresponding to the experts reaches a maximum value sigma;
calculating the matching degree between each expert and the item to be matched according to the expert academic information through the following formula,
M i,j ＝α*MK i,j +β*MJ i,j +γ*ML i,j -δ*MS i,j
wherein M is i,j For the matching degree between the expert i and the item j to be matched, alpha, beta, gamma and delta are constants, MK i,j Matching degree of keywords, MJ, in the scientific research field between expert i and item j to be matched i,j For the matching degree of the labels of the periodical meeting between the expert i and the item j to be matched, ML i,j For the academic level matching degree between the expert i and the item j to be matched, MS i,j Matching degree of social relationship between the expert i and the item j to be matched;
the MS i,j The calculation is carried out by the following formula,
wherein, is a weighted value; delta. For the preparation of a coating i,j Is the correlation degree of the working units between the expert i and the expert v, when the working units are the same, delta i,j The value of (1) is 1, otherwise the value of (0) is obtained; sp is a thesis that the expert i cooperates with the nth expert; n is the number of paper authors; t is t i The weight occupied by expert i; t is t v The weight occupied by the vth expert; k is the serial number of the project applicant corresponding to the project j to be matched; a is the number of the item applicants corresponding to the item j to be matched.
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